{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RNN for Human Activity Recognition - 2D Pose Input\n",
    "\n",
    "This experiment is the classification of human activities using a 2D pose time series dataset and an LSTM RNN.\n",
    "The idea is to prove the concept that using a series of 2D poses, rather than 3D poses or a raw 2D images, can produce an accurate estimation of the behaviour of a person or animal.\n",
    "This is a step towards creating a method of classifying an animal's current behaviour state and predicting it's likely next state, allowing for better interaction with an autonomous mobile robot.\n",
    "\n",
    "## Objectives\n",
    "\n",
    "The aims of this experiment are:\n",
    "\n",
    "-  To determine if 2D pose has comparable accuracy to 3D pose for use in activity recognition. This would allow the use of RGB only cameras for human and animal pose estimation, as opposed to RGBD or a large motion capture dataset.\n",
    "\n",
    "\n",
    "- To determine if  2D pose has comparable accuracy to using raw RGB images for use in activity recognition. This is based on the idea that limiting the input feature vector can help to deal with a limited dataset, as is likely to occur in animal activity recognition, by allowing for a smaller model to be used (citation required).\n",
    "\n",
    "\n",
    "- To verify the concept for use in future works involving behaviour prediction from motion in 2D images.\n",
    "\n",
    "The network used in this experiment is based on that of Guillaume Chevalier, 'LSTMs for Human Activity Recognition, 2016'  https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition, available under the MIT License.\n",
    "Notable changes that have been made (other than accounting for dataset sizes) are:\n",
    " - Adapting for use with a large dataset ordered by class, using random sampling without replacement for mini-batch.  \n",
    " This allows for use of smaller batch sizes when using a dataset ordered by class. \"It has been observed in practice that when using a larger batch there is a significant degradation in the quality of the model, as measured by its ability to generalize\"  \n",
    "      _N.S Keskar, D. Mudigere, et al, 'On Large-Batch Training for Deep Learning: Generalization Gap and Sharp \n",
    "      Minima', ICLR 2017_ https://arxiv.org/abs/1609.04836\n",
    "      \n",
    " - Exponentially decaying learning rate implemented\n",
    "\n",
    "\n",
    "\n",
    "## Dataset overview\n",
    "\n",
    "The dataset consists of pose estimations, made using the software OpenPose (https://github.com/CMU-Perceptual-Computing-Lab/openpose's) on a subset of the Berkeley Multimodal Human Action Database (MHAD) dataset http://tele-immersion.citris-uc.org/berkeley_mhad.\n",
    "\n",
    "This dataset is comprised of 12 subjects doing the following 6 actions for 5 repetitions, filmed from 4 angles, repeated 5 times each.  \n",
    "\n",
    "- JUMPING,\n",
    "- JUMPING_JACKS,\n",
    "- BOXING,\n",
    "- WAVING_2HANDS,\n",
    "- WAVING_1HAND,\n",
    "- CLAPPING_HANDS.\n",
    "\n",
    "In total, there are 1438 videos (2 were missing) made up of 211200 individual frames.\n",
    "\n",
    "The below image is an example of the 4 camera views during the 'boxing' action for subject 1\n",
    "\n",
    "![alt text](images/boxing_all_views.gif.png \"Title\")\n",
    "\n",
    "The input for the LSTM is the 2D position of 18 joints across a timeseries of frames numbering n_steps (window-width), with an associated class label for the frame series.  \n",
    "A single frame's input (where j refers to a joint) is stored as:\n",
    "\n",
    "[  j0_x,  j0_y, j1_x, j1_y , j2_x, j2_y, j3_x, j3_y, j4_x, j4_y, j5_x, j5_y, j6_x, j6_y, j7_x, j7_y, j8_x, j8_y, j9_x, j9_y, j10_x, j10_y, j11_x, j11_y, j12_x, j12_y, j13_x, j13_y, j14_x, j14_y, j15_x, j15_y, j16_x, j16_y, j17_x, j17_y ]\n",
    "\n",
    "For the following experiment, very little preprocessing has been done to the dataset.  \n",
    "The following steps were taken:\n",
    "1. openpose run on individual frames, for each subject, action and view, outputting JSON of 18 joint x and y position keypoints and accuracies per frame\n",
    "2. JSONs converted into txt format, keeping only x and y positions of each frame, action being performed during frame, and order of frames. This is used to create a database of associated activity class number and corresponding series of joint 2D positions\n",
    "3. No further prepossessing was performed.  \n",
    "\n",
    "In some cases, multiple people were detected in each frame, in which only the first detection was used.\n",
    "\n",
    "The data has not been normalised with regards to subject position in the frame, motion across frame (if any), size of the subject, speed of action etc. It is essentially the raw 2D position of each joint viewed from a stationary camera.  \n",
    "In many cases, individual joints were not located and a position of [0.0,0.0] was given for that joint\n",
    "\n",
    "A summary of the dataset used for input is:\n",
    "\n",
    " - 211200 individual images \n",
    " - n_steps = 32 frames (~=1.5s at 22Hz)\n",
    " - Images with noisy pose detection (detection of >=2 people) = 5132  \n",
    " - Training_split = 0.8\n",
    " - Overlap = 0.8125 (26 / 32) ie 26 frame overlap\n",
    "   - Length X_train = 22625 * 32 frames\n",
    "   - Length X_test = 5751 * 32 frames\n",
    "   \n",
    "Note that their is no overlap between test and train sets, which were seperated by activity repetition entirely, before creating the 26 of 32 frame overlap.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "## Training and Results below: \n",
    "Training took approximately 4 mins running on a single GTX1080Ti, and was run for 22,000,000ish iterations with a batch size of 5000  (600 epochs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf  # Version 1.0.0 (some previous versions are used in past commits)\n",
    "from sklearn import metrics\n",
    "import random\n",
    "from random import randint\n",
    "import time\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Useful Constants\n",
    "\n",
    "# Output classes to learn how to classify\n",
    "LABELS = [    \n",
    "    \"JUMPING\",\n",
    "    \"JUMPING_JACKS\",\n",
    "    \"BOXING\",\n",
    "    \"WAVING_2HANDS\",\n",
    "    \"WAVING_1HAND\",\n",
    "    \"CLAPPING_HANDS\"\n",
    "\n",
    "] \n",
    "DATASET_PATH = \"data/HAR_pose_activities/database/\"\n",
    "\n",
    "X_train_path = DATASET_PATH + \"X_train.txt\"\n",
    "X_test_path = DATASET_PATH + \"X_test.txt\"\n",
    "\n",
    "y_train_path = DATASET_PATH + \"Y_train.txt\"\n",
    "y_test_path = DATASET_PATH + \"Y_test.txt\"\n",
    "\n",
    "n_steps = 32 # 32 timesteps per series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Load the networks inputs\n",
    "\n",
    "def load_X(X_path):\n",
    "    file = open(X_path, 'r')\n",
    "    X_ = np.array(\n",
    "        [elem for elem in [\n",
    "            row.split(',') for row in file\n",
    "        ]], \n",
    "        dtype=np.float32\n",
    "    )\n",
    "    file.close()\n",
    "    blocks = int(len(X_) / n_steps)\n",
    "    \n",
    "    X_ = np.array(np.split(X_,blocks))\n",
    "\n",
    "    return X_ \n",
    "\n",
    "# Load the networks outputs\n",
    "\n",
    "def load_y(y_path):\n",
    "    file = open(y_path, 'r')\n",
    "    y_ = np.array(\n",
    "        [elem for elem in [\n",
    "            row.replace('  ', ' ').strip().split(' ') for row in file\n",
    "        ]], \n",
    "        dtype=np.int32\n",
    "    )\n",
    "    file.close()\n",
    "    \n",
    "    # for 0-based indexing \n",
    "    return y_ - 1\n",
    "\n",
    "X_train = load_X(X_train_path)\n",
    "X_test = load_X(X_test_path)\n",
    "#print X_test\n",
    "\n",
    "y_train = load_y(y_train_path)\n",
    "y_test = load_y(y_test_path)\n",
    "# proof that it actually works for the skeptical: replace labelled classes with random classes to train on\n",
    "#for i in range(len(y_train)):\n",
    "#    y_train[i] = randint(0, 5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set Parameters:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(X shape, y shape, every X's mean, every X's standard deviation)\n",
      "((22625, 32, 36), (5751, 1), 251.01117, 126.12204)\n",
      "\n",
      "The dataset has not been preprocessed, is not normalised etc\n"
     ]
    }
   ],
   "source": [
    "# Input Data \n",
    "\n",
    "training_data_count = len(X_train)  # 4519 training series (with 50% overlap between each serie)\n",
    "test_data_count = len(X_test)  # 1197 test series\n",
    "n_input = len(X_train[0][0])  # num input parameters per timestep\n",
    "\n",
    "n_hidden = 34 # Hidden layer num of features\n",
    "n_classes = 6 \n",
    "\n",
    "#updated for learning-rate decay\n",
    "# calculated as: decayed_learning_rate = learning_rate * decay_rate ^ (global_step / decay_steps)\n",
    "decaying_learning_rate = True\n",
    "learning_rate = 0.0025 #used if decaying_learning_rate set to False\n",
    "init_learning_rate = 0.005\n",
    "decay_rate = 0.96 #the base of the exponential in the decay\n",
    "decay_steps = 100000 #used in decay every 60000 steps with a base of 0.96\n",
    "\n",
    "global_step = tf.Variable(0, trainable=False)\n",
    "lambda_loss_amount = 0.0015\n",
    "\n",
    "training_iters = training_data_count *300  # Loop 300 times on the dataset, ie 300 epochs\n",
    "batch_size = 512\n",
    "display_iter = batch_size*8  # To show test set accuracy during training\n",
    "\n",
    "print(\"(X shape, y shape, every X's mean, every X's standard deviation)\")\n",
    "print(X_train.shape, y_test.shape, np.mean(X_test), np.std(X_test))\n",
    "print(\"\\nThe dataset has not been preprocessed, is not normalised etc\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Utility functions for training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def LSTM_RNN(_X, _weights, _biases):\n",
    "    # model architecture based on \"guillaume-chevalier\" and \"aymericdamien\" under the MIT license.\n",
    "\n",
    "    _X = tf.transpose(_X, [1, 0, 2])  # permute n_steps and batch_size\n",
    "    _X = tf.reshape(_X, [-1, n_input])   \n",
    "    # Rectifies Linear Unit activation function used\n",
    "    _X = tf.nn.relu(tf.matmul(_X, _weights['hidden']) + _biases['hidden'])\n",
    "    # Split data because rnn cell needs a list of inputs for the RNN inner loop\n",
    "    _X = tf.split(_X, n_steps, 0) \n",
    "\n",
    "    # Define two stacked LSTM cells (two recurrent layers deep) with tensorflow\n",
    "    lstm_cell_1 = tf.contrib.rnn.BasicLSTMCell(n_hidden, forget_bias=1.0, state_is_tuple=True)\n",
    "    lstm_cell_2 = tf.contrib.rnn.BasicLSTMCell(n_hidden, forget_bias=1.0, state_is_tuple=True)\n",
    "    lstm_cells = tf.contrib.rnn.MultiRNNCell([lstm_cell_1, lstm_cell_2], state_is_tuple=True)\n",
    "    outputs, states = tf.contrib.rnn.static_rnn(lstm_cells, _X, dtype=tf.float32)\n",
    "\n",
    "    # A single output is produced, in style of \"many to one\" classifier, refer to http://karpathy.github.io/2015/05/21/rnn-effectiveness/ for details\n",
    "    lstm_last_output = outputs[-1]\n",
    "    \n",
    "    # Linear activation\n",
    "    return tf.matmul(lstm_last_output, _weights['out']) + _biases['out']\n",
    "\n",
    "\n",
    "def extract_batch_size(_train, _labels, _unsampled, batch_size):\n",
    "    # Fetch a \"batch_size\" amount of data and labels from \"(X|y)_train\" data. \n",
    "    # Elements of each batch are chosen randomly, without replacement, from X_train with corresponding label from Y_train\n",
    "    # unsampled_indices keeps track of sampled data ensuring non-replacement. Resets when remaining datapoints < batch_size    \n",
    "    \n",
    "    shape = list(_train.shape)\n",
    "    shape[0] = batch_size\n",
    "    batch_s = np.empty(shape)\n",
    "    batch_labels = np.empty((batch_size,1)) \n",
    "\n",
    "    for i in range(batch_size):\n",
    "        # Loop index\n",
    "        # index = random sample from _unsampled (indices)\n",
    "        index = random.choice(_unsampled)\n",
    "        batch_s[i] = _train[index] \n",
    "        batch_labels[i] = _labels[index]\n",
    "        _unsampled.remove(index)\n",
    "\n",
    "\n",
    "    return batch_s, batch_labels, _unsampled\n",
    "\n",
    "\n",
    "def one_hot(y_):\n",
    "    # One hot encoding of the network outputs\n",
    "    # e.g.: [[5], [0], [3]] --> [[0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0]]\n",
    "    \n",
    "    y_ = y_.reshape(len(y_))\n",
    "    n_values = int(np.max(y_)) + 1\n",
    "    return np.eye(n_values)[np.array(y_, dtype=np.int32)]  # Returns FLOATS\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build the network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Graph input/output\n",
    "x = tf.placeholder(tf.float32, [None, n_steps, n_input])\n",
    "y = tf.placeholder(tf.float32, [None, n_classes])\n",
    "\n",
    "# Graph weights\n",
    "weights = {\n",
    "    'hidden': tf.Variable(tf.random_normal([n_input, n_hidden])), # Hidden layer weights\n",
    "    'out': tf.Variable(tf.random_normal([n_hidden, n_classes], mean=1.0))\n",
    "}\n",
    "biases = {\n",
    "    'hidden': tf.Variable(tf.random_normal([n_hidden])),\n",
    "    'out': tf.Variable(tf.random_normal([n_classes]))\n",
    "}\n",
    "\n",
    "pred = LSTM_RNN(x, weights, biases)\n",
    "\n",
    "# Loss, optimizer and evaluation\n",
    "l2 = lambda_loss_amount * sum(\n",
    "    tf.nn.l2_loss(tf_var) for tf_var in tf.trainable_variables()\n",
    ") # L2 loss prevents this overkill neural network to overfit the data\n",
    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=pred)) + l2 # Softmax loss\n",
    "if decaying_learning_rate:\n",
    "    learning_rate = tf.train.exponential_decay(init_learning_rate, global_step*batch_size, decay_steps, decay_rate, staircase=True)\n",
    "\n",
    "\n",
    "#decayed_learning_rate = learning_rate * decay_rate ^ (global_step / decay_steps) #exponentially decayed learning rate\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost,global_step=global_step) # Adam Optimizer\n",
    "\n",
    "correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #512:  Learning rate = 0.005000:   Batch Loss = 4.315274, Accuracy = 0.15234375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 3.68809938431, Accuracy = 0.205529466271\n",
      "Iter #4096:  Learning rate = 0.005000:   Batch Loss = 3.057283, Accuracy = 0.263671875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 3.04467487335, Accuracy = 0.262215256691\n",
      "Iter #8192:  Learning rate = 0.005000:   Batch Loss = 2.814128, Accuracy = 0.345703125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.78404188156, Accuracy = 0.383933216333\n",
      "Iter #12288:  Learning rate = 0.005000:   Batch Loss = 2.682125, Accuracy = 0.44921875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.71795201302, Accuracy = 0.407233536243\n",
      "Iter #16384:  Learning rate = 0.005000:   Batch Loss = 2.361879, Accuracy = 0.5\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.44809341431, Accuracy = 0.497652590275\n",
      "Iter #20480:  Learning rate = 0.005000:   Batch Loss = 2.457385, Accuracy = 0.482421875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.40264844894, Accuracy = 0.491218924522\n",
      "Iter #24576:  Learning rate = 0.005000:   Batch Loss = 2.324637, Accuracy = 0.53515625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.33724546432, Accuracy = 0.5679012537\n",
      "Iter #28672:  Learning rate = 0.005000:   Batch Loss = 2.195830, Accuracy = 0.580078125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.18553352356, Accuracy = 0.560772061348\n",
      "Iter #32768:  Learning rate = 0.005000:   Batch Loss = 2.254563, Accuracy = 0.552734375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.43282079697, Accuracy = 0.494001030922\n",
      "Iter #36864:  Learning rate = 0.005000:   Batch Loss = 2.121828, Accuracy = 0.591796875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.07830786705, Accuracy = 0.613284647465\n",
      "Iter #40960:  Learning rate = 0.005000:   Batch Loss = 2.028024, Accuracy = 0.6484375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.98002696037, Accuracy = 0.670492112637\n",
      "Iter #45056:  Learning rate = 0.005000:   Batch Loss = 1.830399, Accuracy = 0.708984375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.96158254147, Accuracy = 0.612936854362\n",
      "Iter #49152:  Learning rate = 0.005000:   Batch Loss = 1.906033, Accuracy = 0.671875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.18290996552, Accuracy = 0.551730155945\n",
      "Iter #53248:  Learning rate = 0.005000:   Batch Loss = 1.980418, Accuracy = 0.62890625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.88367033005, Accuracy = 0.681794464588\n",
      "Iter #57344:  Learning rate = 0.005000:   Batch Loss = 1.784389, Accuracy = 0.71875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.77447557449, Accuracy = 0.728742837906\n",
      "Iter #61440:  Learning rate = 0.005000:   Batch Loss = 1.774224, Accuracy = 0.69140625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.82376849651, Accuracy = 0.65988522768\n",
      "Iter #65536:  Learning rate = 0.005000:   Batch Loss = 1.828996, Accuracy = 0.7109375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.86022770405, Accuracy = 0.663536787033\n",
      "Iter #69632:  Learning rate = 0.005000:   Batch Loss = 1.709383, Accuracy = 0.736328125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.68174505234, Accuracy = 0.733089923859\n",
      "Iter #73728:  Learning rate = 0.005000:   Batch Loss = 2.291216, Accuracy = 0.494140625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.58328270912, Accuracy = 0.406885772943\n",
      "Iter #77824:  Learning rate = 0.005000:   Batch Loss = 2.208295, Accuracy = 0.494140625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.1197783947, Accuracy = 0.525647699833\n",
      "Iter #81920:  Learning rate = 0.005000:   Batch Loss = 1.933757, Accuracy = 0.640625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.96123898029, Accuracy = 0.60511213541\n",
      "Iter #86016:  Learning rate = 0.005000:   Batch Loss = 1.801550, Accuracy = 0.666015625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.72301244736, Accuracy = 0.719527065754\n",
      "Iter #90112:  Learning rate = 0.005000:   Batch Loss = 1.621892, Accuracy = 0.74609375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.6734855175, Accuracy = 0.725439071655\n",
      "Iter #94208:  Learning rate = 0.005000:   Batch Loss = 1.639348, Accuracy = 0.736328125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.76429510117, Accuracy = 0.706311941147\n",
      "Iter #98304:  Learning rate = 0.005000:   Batch Loss = 1.577094, Accuracy = 0.763671875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.63778710365, Accuracy = 0.754303574562\n",
      "Iter #102400:  Learning rate = 0.004800:   Batch Loss = 1.569097, Accuracy = 0.787109375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.54719305038, Accuracy = 0.773604571819\n",
      "Iter #106496:  Learning rate = 0.004800:   Batch Loss = 1.543472, Accuracy = 0.775390625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.56548810005, Accuracy = 0.751695334911\n",
      "Iter #110592:  Learning rate = 0.004800:   Batch Loss = 1.622594, Accuracy = 0.744140625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.62523841858, Accuracy = 0.717266559601\n",
      "Iter #114688:  Learning rate = 0.004800:   Batch Loss = 1.581402, Accuracy = 0.73828125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.4758143425, Accuracy = 0.792905569077\n",
      "Iter #118784:  Learning rate = 0.004800:   Batch Loss = 1.481808, Accuracy = 0.78515625\n",
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      "Iter #122880:  Learning rate = 0.004800:   Batch Loss = 1.553600, Accuracy = 0.75\n",
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      "Iter #126976:  Learning rate = 0.004800:   Batch Loss = 1.975961, Accuracy = 0.591796875\n",
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      "Iter #131072:  Learning rate = 0.004800:   Batch Loss = 1.678564, Accuracy = 0.703125\n",
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      "Iter #135168:  Learning rate = 0.004800:   Batch Loss = 1.644627, Accuracy = 0.7109375\n",
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      "Iter #143360:  Learning rate = 0.004800:   Batch Loss = 1.510630, Accuracy = 0.744140625\n",
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      "Iter #147456:  Learning rate = 0.004800:   Batch Loss = 1.384963, Accuracy = 0.806640625\n",
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      "Iter #151552:  Learning rate = 0.004800:   Batch Loss = 1.398920, Accuracy = 0.78515625\n",
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      "Iter #155648:  Learning rate = 0.004800:   Batch Loss = 1.405812, Accuracy = 0.8046875\n",
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      "Iter #159744:  Learning rate = 0.004800:   Batch Loss = 1.364672, Accuracy = 0.806640625\n",
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      "Iter #163840:  Learning rate = 0.004800:   Batch Loss = 1.324863, Accuracy = 0.841796875\n",
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      "Iter #167936:  Learning rate = 0.004800:   Batch Loss = 1.278751, Accuracy = 0.8671875\n",
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      "Iter #172032:  Learning rate = 0.004800:   Batch Loss = 1.405587, Accuracy = 0.787109375\n",
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      "Iter #176128:  Learning rate = 0.004800:   Batch Loss = 1.353744, Accuracy = 0.80078125\n",
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      "Iter #180224:  Learning rate = 0.004800:   Batch Loss = 1.261146, Accuracy = 0.841796875\n",
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      "Iter #184320:  Learning rate = 0.004800:   Batch Loss = 1.259667, Accuracy = 0.849609375\n",
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      "Iter #188416:  Learning rate = 0.004800:   Batch Loss = 1.255547, Accuracy = 0.84765625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.24595379829, Accuracy = 0.841071128845\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #192512:  Learning rate = 0.004800:   Batch Loss = 1.225653, Accuracy = 0.86328125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.24891602993, Accuracy = 0.837245702744\n",
      "Iter #196608:  Learning rate = 0.004800:   Batch Loss = 1.140967, Accuracy = 0.912109375\n",
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      "Iter #200704:  Learning rate = 0.004608:   Batch Loss = 1.196584, Accuracy = 0.865234375\n",
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      "Iter #204800:  Learning rate = 0.004608:   Batch Loss = 1.271167, Accuracy = 0.8125\n",
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      "Iter #208896:  Learning rate = 0.004608:   Batch Loss = 1.182554, Accuracy = 0.861328125\n",
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      "Iter #212992:  Learning rate = 0.004608:   Batch Loss = 1.229189, Accuracy = 0.849609375\n",
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      "Iter #217088:  Learning rate = 0.004608:   Batch Loss = 1.165783, Accuracy = 0.876953125\n",
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      "Iter #221184:  Learning rate = 0.004608:   Batch Loss = 1.153975, Accuracy = 0.890625\n",
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      "Iter #225280:  Learning rate = 0.004608:   Batch Loss = 1.134334, Accuracy = 0.87890625\n",
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      "Iter #229376:  Learning rate = 0.004608:   Batch Loss = 1.113050, Accuracy = 0.896484375\n",
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      "Iter #233472:  Learning rate = 0.004608:   Batch Loss = 1.124773, Accuracy = 0.876953125\n",
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      "Iter #237568:  Learning rate = 0.004608:   Batch Loss = 1.191074, Accuracy = 0.853515625\n",
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      "Iter #241664:  Learning rate = 0.004608:   Batch Loss = 1.094016, Accuracy = 0.896484375\n",
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      "Iter #245760:  Learning rate = 0.004608:   Batch Loss = 1.160288, Accuracy = 0.869140625\n",
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      "Iter #249856:  Learning rate = 0.004608:   Batch Loss = 1.197851, Accuracy = 0.8515625\n",
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      "Iter #253952:  Learning rate = 0.004608:   Batch Loss = 1.078244, Accuracy = 0.88671875\n",
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      "Iter #258048:  Learning rate = 0.004608:   Batch Loss = 1.106227, Accuracy = 0.859375\n",
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      "Iter #262144:  Learning rate = 0.004608:   Batch Loss = 1.090835, Accuracy = 0.8984375\n",
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      "Iter #266240:  Learning rate = 0.004608:   Batch Loss = 1.164523, Accuracy = 0.857421875\n",
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      "Iter #270336:  Learning rate = 0.004608:   Batch Loss = 1.078694, Accuracy = 0.876953125\n",
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      "Iter #274432:  Learning rate = 0.004608:   Batch Loss = 1.059673, Accuracy = 0.87890625\n",
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      "Iter #278528:  Learning rate = 0.004608:   Batch Loss = 1.126772, Accuracy = 0.84375\n",
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      "Iter #282624:  Learning rate = 0.004608:   Batch Loss = 1.097116, Accuracy = 0.861328125\n",
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      "Iter #286720:  Learning rate = 0.004608:   Batch Loss = 1.086557, Accuracy = 0.8671875\n",
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      "Iter #290816:  Learning rate = 0.004608:   Batch Loss = 1.184409, Accuracy = 0.861328125\n",
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      "Iter #294912:  Learning rate = 0.004608:   Batch Loss = 1.116552, Accuracy = 0.857421875\n",
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      "Iter #299008:  Learning rate = 0.004608:   Batch Loss = 1.091724, Accuracy = 0.890625\n",
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      "Iter #303104:  Learning rate = 0.004424:   Batch Loss = 0.986324, Accuracy = 0.923828125\n",
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      "Iter #307200:  Learning rate = 0.004424:   Batch Loss = 0.998030, Accuracy = 0.89453125\n",
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      "Iter #311296:  Learning rate = 0.004424:   Batch Loss = 0.980945, Accuracy = 0.91796875\n",
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      "Iter #315392:  Learning rate = 0.004424:   Batch Loss = 1.009427, Accuracy = 0.88671875\n",
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      "Iter #319488:  Learning rate = 0.004424:   Batch Loss = 1.058067, Accuracy = 0.8671875\n",
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      "Iter #323584:  Learning rate = 0.004424:   Batch Loss = 0.994620, Accuracy = 0.90625\n",
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      "Iter #327680:  Learning rate = 0.004424:   Batch Loss = 1.095577, Accuracy = 0.853515625\n",
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      "Iter #331776:  Learning rate = 0.004424:   Batch Loss = 1.038876, Accuracy = 0.873046875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.989422976971, Accuracy = 0.896713614464\n",
      "Iter #335872:  Learning rate = 0.004424:   Batch Loss = 1.045440, Accuracy = 0.87890625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.99707865715, Accuracy = 0.888193368912\n",
      "Iter #339968:  Learning rate = 0.004424:   Batch Loss = 1.273818, Accuracy = 0.83203125\n",
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      "Iter #344064:  Learning rate = 0.004424:   Batch Loss = 1.150636, Accuracy = 0.830078125\n",
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      "Iter #348160:  Learning rate = 0.004424:   Batch Loss = 1.067706, Accuracy = 0.85546875\n",
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      "Iter #352256:  Learning rate = 0.004424:   Batch Loss = 1.048522, Accuracy = 0.865234375\n",
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      "Iter #356352:  Learning rate = 0.004424:   Batch Loss = 1.012132, Accuracy = 0.904296875\n",
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      "Iter #360448:  Learning rate = 0.004424:   Batch Loss = 0.989047, Accuracy = 0.888671875\n",
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      "Iter #372736:  Learning rate = 0.004424:   Batch Loss = 0.930576, Accuracy = 0.91796875\n",
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      "Iter #376832:  Learning rate = 0.004424:   Batch Loss = 0.947550, Accuracy = 0.90625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.970808267593, Accuracy = 0.905755519867\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #380928:  Learning rate = 0.004424:   Batch Loss = 0.952235, Accuracy = 0.90625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.952122449875, Accuracy = 0.900712907314\n",
      "Iter #385024:  Learning rate = 0.004424:   Batch Loss = 0.912896, Accuracy = 0.921875\n",
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      "Iter #389120:  Learning rate = 0.004424:   Batch Loss = 0.947593, Accuracy = 0.89453125\n",
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      "Iter #393216:  Learning rate = 0.004424:   Batch Loss = 0.968514, Accuracy = 0.90625\n",
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      "Iter #397312:  Learning rate = 0.004424:   Batch Loss = 0.961573, Accuracy = 0.896484375\n",
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      "Iter #401408:  Learning rate = 0.004247:   Batch Loss = 0.991264, Accuracy = 0.880859375\n",
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      "Iter #405504:  Learning rate = 0.004247:   Batch Loss = 0.994957, Accuracy = 0.89453125\n",
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      "Iter #409600:  Learning rate = 0.004247:   Batch Loss = 1.031549, Accuracy = 0.875\n",
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      "Iter #413696:  Learning rate = 0.004247:   Batch Loss = 0.914717, Accuracy = 0.919921875\n",
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      "Iter #417792:  Learning rate = 0.004247:   Batch Loss = 0.957765, Accuracy = 0.890625\n",
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      "Iter #421888:  Learning rate = 0.004247:   Batch Loss = 0.931691, Accuracy = 0.91015625\n",
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      "Iter #425984:  Learning rate = 0.004247:   Batch Loss = 0.894961, Accuracy = 0.919921875\n",
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      "Iter #430080:  Learning rate = 0.004247:   Batch Loss = 0.954015, Accuracy = 0.87890625\n",
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      "Iter #434176:  Learning rate = 0.004247:   Batch Loss = 0.965562, Accuracy = 0.8984375\n",
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      "Iter #438272:  Learning rate = 0.004247:   Batch Loss = 0.998388, Accuracy = 0.865234375\n",
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      "Iter #442368:  Learning rate = 0.004247:   Batch Loss = 1.025146, Accuracy = 0.83984375\n",
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      "Iter #446464:  Learning rate = 0.004247:   Batch Loss = 0.858868, Accuracy = 0.93359375\n",
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      "Iter #450560:  Learning rate = 0.004247:   Batch Loss = 0.871211, Accuracy = 0.921875\n",
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      "Iter #454656:  Learning rate = 0.004247:   Batch Loss = 0.878051, Accuracy = 0.927734375\n",
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      "Iter #458752:  Learning rate = 0.004247:   Batch Loss = 0.876325, Accuracy = 0.927734375\n",
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      "Iter #466944:  Learning rate = 0.004247:   Batch Loss = 0.909490, Accuracy = 0.921875\n",
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      "Iter #471040:  Learning rate = 0.004247:   Batch Loss = 0.898837, Accuracy = 0.91796875\n",
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      "Iter #475136:  Learning rate = 0.004247:   Batch Loss = 0.956898, Accuracy = 0.896484375\n",
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      "Iter #479232:  Learning rate = 0.004247:   Batch Loss = 0.884311, Accuracy = 0.93359375\n",
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      "Iter #483328:  Learning rate = 0.004247:   Batch Loss = 0.884706, Accuracy = 0.9140625\n",
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      "Iter #487424:  Learning rate = 0.004247:   Batch Loss = 0.834675, Accuracy = 0.943359375\n",
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      "Iter #495616:  Learning rate = 0.004247:   Batch Loss = 0.820159, Accuracy = 0.947265625\n",
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      "Iter #499712:  Learning rate = 0.004247:   Batch Loss = 0.838289, Accuracy = 0.921875\n",
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      "Iter #507904:  Learning rate = 0.004077:   Batch Loss = 0.829502, Accuracy = 0.94140625\n",
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      "Iter #512000:  Learning rate = 0.004077:   Batch Loss = 0.885287, Accuracy = 0.900390625\n",
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      "Iter #516096:  Learning rate = 0.004077:   Batch Loss = 0.833354, Accuracy = 0.935546875\n",
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      "Iter #520192:  Learning rate = 0.004077:   Batch Loss = 0.825035, Accuracy = 0.91796875\n",
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      "Iter #524288:  Learning rate = 0.004077:   Batch Loss = 0.866789, Accuracy = 0.927734375\n",
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      "Iter #528384:  Learning rate = 0.004077:   Batch Loss = 1.575331, Accuracy = 0.65625\n",
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      "Iter #532480:  Learning rate = 0.004077:   Batch Loss = 1.152835, Accuracy = 0.779296875\n",
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      "Iter #536576:  Learning rate = 0.004077:   Batch Loss = 1.070047, Accuracy = 0.837890625\n",
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      "Iter #540672:  Learning rate = 0.004077:   Batch Loss = 0.956712, Accuracy = 0.876953125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.944431066513, Accuracy = 0.87967312336\n",
      "Iter #544768:  Learning rate = 0.004077:   Batch Loss = 0.909610, Accuracy = 0.8984375\n",
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      "Iter #548864:  Learning rate = 0.004077:   Batch Loss = 0.888367, Accuracy = 0.90234375\n",
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      "Iter #552960:  Learning rate = 0.004077:   Batch Loss = 0.925037, Accuracy = 0.884765625\n",
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      "Iter #557056:  Learning rate = 0.004077:   Batch Loss = 0.866913, Accuracy = 0.916015625\n",
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      "Iter #561152:  Learning rate = 0.004077:   Batch Loss = 0.884819, Accuracy = 0.9140625\n",
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      "Iter #565248:  Learning rate = 0.004077:   Batch Loss = 0.876426, Accuracy = 0.900390625\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #569344:  Learning rate = 0.004077:   Batch Loss = 0.991116, Accuracy = 0.8828125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.09497725964, Accuracy = 0.833420276642\n",
      "Iter #573440:  Learning rate = 0.004077:   Batch Loss = 0.894690, Accuracy = 0.892578125\n",
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      "Iter #577536:  Learning rate = 0.004077:   Batch Loss = 0.892936, Accuracy = 0.892578125\n",
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      "Iter #581632:  Learning rate = 0.004077:   Batch Loss = 0.945477, Accuracy = 0.880859375\n",
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      "Iter #585728:  Learning rate = 0.004077:   Batch Loss = 0.863998, Accuracy = 0.900390625\n",
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      "Iter #589824:  Learning rate = 0.004077:   Batch Loss = 0.894184, Accuracy = 0.90234375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.887295603752, Accuracy = 0.894974768162\n",
      "Iter #593920:  Learning rate = 0.004077:   Batch Loss = 0.866985, Accuracy = 0.904296875\n",
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      "Iter #598016:  Learning rate = 0.004077:   Batch Loss = 0.911512, Accuracy = 0.8828125\n",
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      "Iter #602112:  Learning rate = 0.003914:   Batch Loss = 0.839528, Accuracy = 0.921875\n",
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      "Iter #606208:  Learning rate = 0.003914:   Batch Loss = 0.828426, Accuracy = 0.919921875\n",
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      "Iter #610304:  Learning rate = 0.003914:   Batch Loss = 0.814400, Accuracy = 0.923828125\n",
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      "Iter #614400:  Learning rate = 0.003914:   Batch Loss = 0.809834, Accuracy = 0.931640625\n",
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      "Iter #618496:  Learning rate = 0.003914:   Batch Loss = 0.823595, Accuracy = 0.921875\n",
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      "Iter #622592:  Learning rate = 0.003914:   Batch Loss = 0.920957, Accuracy = 0.880859375\n",
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      "Iter #626688:  Learning rate = 0.003914:   Batch Loss = 0.824243, Accuracy = 0.90625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.848239541054, Accuracy = 0.891671001911\n",
      "Iter #630784:  Learning rate = 0.003914:   Batch Loss = 0.815887, Accuracy = 0.9375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.818643808365, Accuracy = 0.915145218372\n",
      "Iter #634880:  Learning rate = 0.003914:   Batch Loss = 0.827680, Accuracy = 0.91796875\n",
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      "Iter #638976:  Learning rate = 0.003914:   Batch Loss = 0.812697, Accuracy = 0.939453125\n",
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      "Iter #643072:  Learning rate = 0.003914:   Batch Loss = 0.805460, Accuracy = 0.927734375\n",
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      "Iter #647168:  Learning rate = 0.003914:   Batch Loss = 0.800154, Accuracy = 0.9296875\n",
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      "Iter #651264:  Learning rate = 0.003914:   Batch Loss = 0.837086, Accuracy = 0.908203125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.85007750988, Accuracy = 0.89184486866\n",
      "Iter #655360:  Learning rate = 0.003914:   Batch Loss = 0.796072, Accuracy = 0.92578125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.841115355492, Accuracy = 0.907320439816\n",
      "Iter #659456:  Learning rate = 0.003914:   Batch Loss = 0.851598, Accuracy = 0.9140625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.839597702026, Accuracy = 0.911667525768\n",
      "Iter #663552:  Learning rate = 0.003914:   Batch Loss = 0.776145, Accuracy = 0.939453125\n",
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      "Iter #667648:  Learning rate = 0.003914:   Batch Loss = 0.806620, Accuracy = 0.91015625\n",
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      "Iter #671744:  Learning rate = 0.003914:   Batch Loss = 0.804169, Accuracy = 0.908203125\n",
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      "Iter #675840:  Learning rate = 0.003914:   Batch Loss = 0.817089, Accuracy = 0.9140625\n",
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      "Iter #679936:  Learning rate = 0.003914:   Batch Loss = 0.796127, Accuracy = 0.904296875\n",
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      "Iter #684032:  Learning rate = 0.003914:   Batch Loss = 0.812355, Accuracy = 0.919921875\n",
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      "Iter #688128:  Learning rate = 0.003914:   Batch Loss = 0.787167, Accuracy = 0.923828125\n",
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      "Iter #692224:  Learning rate = 0.003914:   Batch Loss = 0.804830, Accuracy = 0.9296875\n",
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      "Iter #696320:  Learning rate = 0.003914:   Batch Loss = 0.758409, Accuracy = 0.947265625\n",
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      "Iter #700416:  Learning rate = 0.003757:   Batch Loss = 0.758824, Accuracy = 0.9296875\n",
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      "Iter #704512:  Learning rate = 0.003757:   Batch Loss = 0.783976, Accuracy = 0.912109375\n",
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      "Iter #708608:  Learning rate = 0.003757:   Batch Loss = 0.752631, Accuracy = 0.9375\n",
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      "Iter #712704:  Learning rate = 0.003757:   Batch Loss = 0.790168, Accuracy = 0.9140625\n",
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      "Iter #716800:  Learning rate = 0.003757:   Batch Loss = 0.743440, Accuracy = 0.939453125\n",
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      "Iter #720896:  Learning rate = 0.003757:   Batch Loss = 0.802323, Accuracy = 0.921875\n",
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      "Iter #724992:  Learning rate = 0.003757:   Batch Loss = 0.755107, Accuracy = 0.9375\n",
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      "Iter #729088:  Learning rate = 0.003757:   Batch Loss = 0.861987, Accuracy = 0.884765625\n",
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      "Iter #733184:  Learning rate = 0.003757:   Batch Loss = 0.803933, Accuracy = 0.908203125\n",
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      "Iter #745472:  Learning rate = 0.003757:   Batch Loss = 0.754050, Accuracy = 0.943359375\n",
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      "Iter #749568:  Learning rate = 0.003757:   Batch Loss = 0.776635, Accuracy = 0.923828125\n",
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      "Iter #753664:  Learning rate = 0.003757:   Batch Loss = 0.780404, Accuracy = 0.9296875\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #757760:  Learning rate = 0.003757:   Batch Loss = 0.825089, Accuracy = 0.91015625\n",
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      "Iter #761856:  Learning rate = 0.003757:   Batch Loss = 0.950108, Accuracy = 0.857421875\n",
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      "Iter #765952:  Learning rate = 0.003757:   Batch Loss = 0.832553, Accuracy = 0.884765625\n",
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      "Iter #770048:  Learning rate = 0.003757:   Batch Loss = 0.810594, Accuracy = 0.91015625\n",
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      "Iter #774144:  Learning rate = 0.003757:   Batch Loss = 0.757618, Accuracy = 0.94140625\n",
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      "Iter #778240:  Learning rate = 0.003757:   Batch Loss = 0.780720, Accuracy = 0.91796875\n",
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      "Iter #782336:  Learning rate = 0.003757:   Batch Loss = 0.831041, Accuracy = 0.900390625\n",
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      "Iter #786432:  Learning rate = 0.003757:   Batch Loss = 0.847450, Accuracy = 0.8828125\n",
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      "Iter #790528:  Learning rate = 0.003757:   Batch Loss = 0.800875, Accuracy = 0.90625\n",
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      "Iter #794624:  Learning rate = 0.003757:   Batch Loss = 0.823337, Accuracy = 0.896484375\n",
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      "Iter #798720:  Learning rate = 0.003757:   Batch Loss = 0.787568, Accuracy = 0.91015625\n",
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      "Iter #802816:  Learning rate = 0.003607:   Batch Loss = 0.782602, Accuracy = 0.927734375\n",
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      "Iter #806912:  Learning rate = 0.003607:   Batch Loss = 0.779580, Accuracy = 0.90625\n",
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      "Iter #811008:  Learning rate = 0.003607:   Batch Loss = 0.751130, Accuracy = 0.921875\n",
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      "Iter #815104:  Learning rate = 0.003607:   Batch Loss = 0.766130, Accuracy = 0.93359375\n",
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      "Iter #819200:  Learning rate = 0.003607:   Batch Loss = 0.803578, Accuracy = 0.88671875\n",
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      "Iter #823296:  Learning rate = 0.003607:   Batch Loss = 0.727057, Accuracy = 0.93359375\n",
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      "Iter #827392:  Learning rate = 0.003607:   Batch Loss = 0.774633, Accuracy = 0.91015625\n",
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      "Iter #831488:  Learning rate = 0.003607:   Batch Loss = 0.778091, Accuracy = 0.91015625\n",
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      "Iter #835584:  Learning rate = 0.003607:   Batch Loss = 0.737282, Accuracy = 0.927734375\n",
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      "Iter #839680:  Learning rate = 0.003607:   Batch Loss = 1.044268, Accuracy = 0.818359375\n",
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      "Iter #843776:  Learning rate = 0.003607:   Batch Loss = 0.865865, Accuracy = 0.88671875\n",
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      "Iter #847872:  Learning rate = 0.003607:   Batch Loss = 0.793591, Accuracy = 0.9140625\n",
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      "Iter #851968:  Learning rate = 0.003607:   Batch Loss = 0.722958, Accuracy = 0.935546875\n",
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      "Iter #856064:  Learning rate = 0.003607:   Batch Loss = 0.770231, Accuracy = 0.912109375\n",
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      "Iter #860160:  Learning rate = 0.003607:   Batch Loss = 0.761634, Accuracy = 0.90625\n",
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      "Iter #864256:  Learning rate = 0.003607:   Batch Loss = 0.881197, Accuracy = 0.888671875\n",
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      "Iter #868352:  Learning rate = 0.003607:   Batch Loss = 0.759176, Accuracy = 0.921875\n",
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      "Iter #872448:  Learning rate = 0.003607:   Batch Loss = 0.791288, Accuracy = 0.91015625\n",
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      "Iter #876544:  Learning rate = 0.003607:   Batch Loss = 0.710074, Accuracy = 0.947265625\n",
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      "Iter #880640:  Learning rate = 0.003607:   Batch Loss = 0.721644, Accuracy = 0.931640625\n",
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      "Iter #884736:  Learning rate = 0.003607:   Batch Loss = 0.763135, Accuracy = 0.921875\n",
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      "Iter #888832:  Learning rate = 0.003607:   Batch Loss = 0.758742, Accuracy = 0.9140625\n",
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      "Iter #892928:  Learning rate = 0.003607:   Batch Loss = 0.800088, Accuracy = 0.90625\n",
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      "Iter #897024:  Learning rate = 0.003607:   Batch Loss = 0.708966, Accuracy = 0.9375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.760018110275, Accuracy = 0.909580945969\n",
      "Iter #901120:  Learning rate = 0.003463:   Batch Loss = 0.850657, Accuracy = 0.8828125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.795417368412, Accuracy = 0.902451753616\n",
      "Iter #905216:  Learning rate = 0.003463:   Batch Loss = 0.750533, Accuracy = 0.91015625\n",
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      "Iter #909312:  Learning rate = 0.003463:   Batch Loss = 0.703903, Accuracy = 0.931640625\n",
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      "Iter #913408:  Learning rate = 0.003463:   Batch Loss = 0.742773, Accuracy = 0.912109375\n",
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      "Iter #917504:  Learning rate = 0.003463:   Batch Loss = 0.757043, Accuracy = 0.92578125\n",
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      "Iter #921600:  Learning rate = 0.003463:   Batch Loss = 0.727461, Accuracy = 0.923828125\n",
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      "Iter #925696:  Learning rate = 0.003463:   Batch Loss = 0.694188, Accuracy = 0.93359375\n",
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      "Iter #929792:  Learning rate = 0.003463:   Batch Loss = 0.714977, Accuracy = 0.9375\n",
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      "Iter #933888:  Learning rate = 0.003463:   Batch Loss = 0.737835, Accuracy = 0.919921875\n",
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      "Iter #937984:  Learning rate = 0.003463:   Batch Loss = 0.747734, Accuracy = 0.91796875\n",
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      "Iter #942080:  Learning rate = 0.003463:   Batch Loss = 0.697164, Accuracy = 0.935546875\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #946176:  Learning rate = 0.003463:   Batch Loss = 0.695094, Accuracy = 0.94140625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.720028996468, Accuracy = 0.935315608978\n",
      "Iter #950272:  Learning rate = 0.003463:   Batch Loss = 0.674766, Accuracy = 0.943359375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.738963782787, Accuracy = 0.91479742527\n",
      "Iter #954368:  Learning rate = 0.003463:   Batch Loss = 0.699598, Accuracy = 0.931640625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.76727950573, Accuracy = 0.903321146965\n",
      "Iter #958464:  Learning rate = 0.003463:   Batch Loss = 0.694827, Accuracy = 0.94140625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.746849179268, Accuracy = 0.91410189867\n",
      "Iter #962560:  Learning rate = 0.003463:   Batch Loss = 0.965101, Accuracy = 0.837890625\n",
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      "Iter #966656:  Learning rate = 0.003463:   Batch Loss = 0.896669, Accuracy = 0.876953125\n",
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      "Iter #1101824:  Learning rate = 0.003191:   Batch Loss = 0.681274, Accuracy = 0.9296875\n",
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      "Iter #1105920:  Learning rate = 0.003191:   Batch Loss = 0.658529, Accuracy = 0.947265625\n",
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      "Iter #1110016:  Learning rate = 0.003191:   Batch Loss = 0.662977, Accuracy = 0.943359375\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #1134592:  Learning rate = 0.003191:   Batch Loss = 0.709217, Accuracy = 0.927734375\n",
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      "Iter #1159168:  Learning rate = 0.003191:   Batch Loss = 0.956906, Accuracy = 0.833984375\n",
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      "Iter #1167360:  Learning rate = 0.003191:   Batch Loss = 0.749933, Accuracy = 0.916015625\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #1323008:  Learning rate = 0.002941:   Batch Loss = 0.616287, Accuracy = 0.955078125\n",
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      "Iter #1355776:  Learning rate = 0.002941:   Batch Loss = 0.657728, Accuracy = 0.9375\n",
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      "Iter #1384448:  Learning rate = 0.002941:   Batch Loss = 0.662293, Accuracy = 0.9375\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #1511424:  Learning rate = 0.002710:   Batch Loss = 0.580128, Accuracy = 0.96484375\n",
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      "Iter #1601536:  Learning rate = 0.002602:   Batch Loss = 0.589864, Accuracy = 0.94921875\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #1699840:  Learning rate = 0.002602:   Batch Loss = 0.590384, Accuracy = 0.943359375\n",
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      "Iter #1851392:  Learning rate = 0.002398:   Batch Loss = 0.563448, Accuracy = 0.951171875\n",
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      "Iter #1855488:  Learning rate = 0.002398:   Batch Loss = 0.665148, Accuracy = 0.921875\n",
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      "Iter #1863680:  Learning rate = 0.002398:   Batch Loss = 0.563431, Accuracy = 0.951171875\n",
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      "Iter #1884160:  Learning rate = 0.002398:   Batch Loss = 0.579393, Accuracy = 0.943359375\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #1888256:  Learning rate = 0.002398:   Batch Loss = 0.610111, Accuracy = 0.923828125\n",
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      "Iter #1892352:  Learning rate = 0.002398:   Batch Loss = 0.565088, Accuracy = 0.955078125\n",
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      "Iter #1941504:  Learning rate = 0.002302:   Batch Loss = 0.546451, Accuracy = 0.95703125\n",
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      "Iter #1961984:  Learning rate = 0.002302:   Batch Loss = 0.580893, Accuracy = 0.9375\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iter #2076672:  Learning rate = 0.002210:   Batch Loss = 0.515174, Accuracy = 0.962890625\n",
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      "Iter #2142208:  Learning rate = 0.002122:   Batch Loss = 0.522306, Accuracy = 0.947265625\n",
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      "Iter #2236416:  Learning rate = 0.002037:   Batch Loss = 0.503918, Accuracy = 0.958984375\n",
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      "Iter #2240512:  Learning rate = 0.002037:   Batch Loss = 0.470373, Accuracy = 0.9765625\n",
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      "Iter #2244608:  Learning rate = 0.002037:   Batch Loss = 0.558792, Accuracy = 0.947265625\n",
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      "Iter #2248704:  Learning rate = 0.002037:   Batch Loss = 0.542916, Accuracy = 0.93359375\n",
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      "Iter #2252800:  Learning rate = 0.002037:   Batch Loss = 0.504780, Accuracy = 0.96875\n",
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      "Iter #2256896:  Learning rate = 0.002037:   Batch Loss = 0.463772, Accuracy = 0.978515625\n",
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      "Iter #2260992:  Learning rate = 0.002037:   Batch Loss = 0.529583, Accuracy = 0.955078125\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #2265088:  Learning rate = 0.002037:   Batch Loss = 0.492740, Accuracy = 0.96484375\n",
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      "Iter #2269184:  Learning rate = 0.002037:   Batch Loss = 0.479516, Accuracy = 0.98046875\n",
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      "Iter #2273280:  Learning rate = 0.002037:   Batch Loss = 0.523295, Accuracy = 0.96484375\n",
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      "Iter #2277376:  Learning rate = 0.002037:   Batch Loss = 0.541209, Accuracy = 0.94921875\n",
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      "Iter #2285568:  Learning rate = 0.002037:   Batch Loss = 0.475449, Accuracy = 0.9765625\n",
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      "Iter #2289664:  Learning rate = 0.002037:   Batch Loss = 0.525737, Accuracy = 0.94921875\n",
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      "Iter #2293760:  Learning rate = 0.002037:   Batch Loss = 0.503999, Accuracy = 0.96484375\n",
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      "Iter #2297856:  Learning rate = 0.002037:   Batch Loss = 0.515294, Accuracy = 0.94921875\n",
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      "Iter #2318336:  Learning rate = 0.001955:   Batch Loss = 0.504136, Accuracy = 0.955078125\n",
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      "Iter #2322432:  Learning rate = 0.001955:   Batch Loss = 0.465527, Accuracy = 0.9765625\n",
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      "Iter #2326528:  Learning rate = 0.001955:   Batch Loss = 0.517820, Accuracy = 0.95703125\n",
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      "Iter #2330624:  Learning rate = 0.001955:   Batch Loss = 0.493787, Accuracy = 0.96484375\n",
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      "Iter #2338816:  Learning rate = 0.001955:   Batch Loss = 0.501999, Accuracy = 0.95703125\n",
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      "Iter #2342912:  Learning rate = 0.001955:   Batch Loss = 0.522065, Accuracy = 0.958984375\n",
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      "Iter #2355200:  Learning rate = 0.001955:   Batch Loss = 0.520256, Accuracy = 0.95703125\n",
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      "Iter #2359296:  Learning rate = 0.001955:   Batch Loss = 0.501677, Accuracy = 0.96484375\n",
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      "Iter #2363392:  Learning rate = 0.001955:   Batch Loss = 0.499235, Accuracy = 0.95703125\n",
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      "Iter #2367488:  Learning rate = 0.001955:   Batch Loss = 0.486072, Accuracy = 0.962890625\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #2453504:  Learning rate = 0.001877:   Batch Loss = 0.481784, Accuracy = 0.962890625\n",
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      "Iter #2465792:  Learning rate = 0.001877:   Batch Loss = 0.509301, Accuracy = 0.955078125\n",
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      "Iter #2519040:  Learning rate = 0.001802:   Batch Loss = 0.470555, Accuracy = 0.962890625\n",
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      "Iter #2523136:  Learning rate = 0.001802:   Batch Loss = 0.475472, Accuracy = 0.96484375\n",
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      "Iter #2527232:  Learning rate = 0.001802:   Batch Loss = 0.480935, Accuracy = 0.97265625\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #2641920:  Learning rate = 0.001730:   Batch Loss = 0.449749, Accuracy = 0.97265625\n",
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      "Iter #2646016:  Learning rate = 0.001730:   Batch Loss = 0.482257, Accuracy = 0.962890625\n",
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      "Iter #2654208:  Learning rate = 0.001730:   Batch Loss = 0.491371, Accuracy = 0.958984375\n",
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      "Iter #2658304:  Learning rate = 0.001730:   Batch Loss = 0.574480, Accuracy = 0.935546875\n",
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      "Iter #2736128:  Learning rate = 0.001661:   Batch Loss = 0.463555, Accuracy = 0.974609375\n",
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      "Iter #2744320:  Learning rate = 0.001661:   Batch Loss = 0.444828, Accuracy = 0.9765625\n",
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      "Iter #2785280:  Learning rate = 0.001661:   Batch Loss = 0.434350, Accuracy = 0.9765625\n",
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      "Iter #2793472:  Learning rate = 0.001661:   Batch Loss = 0.428511, Accuracy = 0.978515625\n",
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      "Iter #2797568:  Learning rate = 0.001661:   Batch Loss = 0.493277, Accuracy = 0.95703125\n",
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      "Iter #2801664:  Learning rate = 0.001594:   Batch Loss = 0.533516, Accuracy = 0.94921875\n",
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      "Iter #2805760:  Learning rate = 0.001594:   Batch Loss = 0.457656, Accuracy = 0.96484375\n",
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      "Iter #2809856:  Learning rate = 0.001594:   Batch Loss = 0.449718, Accuracy = 0.966796875\n",
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      "Iter #2822144:  Learning rate = 0.001594:   Batch Loss = 0.437471, Accuracy = 0.97265625\n",
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      "Iter #2826240:  Learning rate = 0.001594:   Batch Loss = 0.451414, Accuracy = 0.978515625\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #2830336:  Learning rate = 0.001594:   Batch Loss = 0.460027, Accuracy = 0.96484375\n",
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      "Iter #2846720:  Learning rate = 0.001594:   Batch Loss = 0.458719, Accuracy = 0.970703125\n",
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      "Iter #2850816:  Learning rate = 0.001594:   Batch Loss = 0.452304, Accuracy = 0.96875\n",
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      "Iter #2863104:  Learning rate = 0.001594:   Batch Loss = 0.485803, Accuracy = 0.9609375\n",
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      "Iter #2895872:  Learning rate = 0.001594:   Batch Loss = 0.514820, Accuracy = 0.953125\n",
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      "Iter #2899968:  Learning rate = 0.001594:   Batch Loss = 0.484875, Accuracy = 0.951171875\n",
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      "Iter #2904064:  Learning rate = 0.001531:   Batch Loss = 0.472148, Accuracy = 0.958984375\n",
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      "Iter #2908160:  Learning rate = 0.001531:   Batch Loss = 0.423385, Accuracy = 0.98046875\n",
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      "Iter #2912256:  Learning rate = 0.001531:   Batch Loss = 0.423595, Accuracy = 0.98046875\n",
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      "Iter #2916352:  Learning rate = 0.001531:   Batch Loss = 0.437081, Accuracy = 0.96484375\n",
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      "Iter #2920448:  Learning rate = 0.001531:   Batch Loss = 0.508516, Accuracy = 0.94921875\n",
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      "Iter #2928640:  Learning rate = 0.001531:   Batch Loss = 0.488831, Accuracy = 0.96484375\n",
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      "Iter #2961408:  Learning rate = 0.001531:   Batch Loss = 0.426170, Accuracy = 0.974609375\n",
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      "Iter #3014656:  Learning rate = 0.001469:   Batch Loss = 0.472668, Accuracy = 0.958984375\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #3018752:  Learning rate = 0.001469:   Batch Loss = 0.441309, Accuracy = 0.9765625\n",
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      "Iter #3051520:  Learning rate = 0.001469:   Batch Loss = 0.441932, Accuracy = 0.97265625\n",
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      "Iter #3055616:  Learning rate = 0.001469:   Batch Loss = 0.434469, Accuracy = 0.970703125\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #3207168:  Learning rate = 0.001354:   Batch Loss = 0.431710, Accuracy = 0.97265625\n",
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     ]
    },
    {
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     "text": [
      "Iter #3395584:  Learning rate = 0.001300:   Batch Loss = 0.397809, Accuracy = 0.984375\n",
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      "Iter #3579904:  Learning rate = 0.001198:   Batch Loss = 0.414716, Accuracy = 0.970703125\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #3584000:  Learning rate = 0.001198:   Batch Loss = 0.396957, Accuracy = 0.98046875\n",
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      "Iter #3608576:  Learning rate = 0.001150:   Batch Loss = 0.409769, Accuracy = 0.978515625\n",
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      "Iter #3702784:  Learning rate = 0.001104:   Batch Loss = 0.388819, Accuracy = 0.98046875\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #3772416:  Learning rate = 0.001104:   Batch Loss = 0.363726, Accuracy = 0.990234375\n",
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      "Iter #3809280:  Learning rate = 0.001060:   Batch Loss = 0.398484, Accuracy = 0.982421875\n",
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      "Iter #3846144:  Learning rate = 0.001060:   Batch Loss = 0.358900, Accuracy = 0.990234375\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iter #3960832:  Learning rate = 0.001018:   Batch Loss = 0.365578, Accuracy = 0.990234375\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iter #4149248:  Learning rate = 0.000938:   Batch Loss = 0.391943, Accuracy = 0.9765625\n",
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     ]
    },
    {
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     "text": [
      "Iter #4337664:  Learning rate = 0.000864:   Batch Loss = 0.358813, Accuracy = 0.98828125\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #4526080:  Learning rate = 0.000796:   Batch Loss = 0.363955, Accuracy = 0.98046875\n",
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     ]
    },
    {
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     "text": [
      "Iter #4714496:  Learning rate = 0.000734:   Batch Loss = 0.351102, Accuracy = 0.990234375\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #4902912:  Learning rate = 0.000676:   Batch Loss = 0.325324, Accuracy = 0.99609375\n",
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     ]
    },
    {
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     "text": [
      "Iter #5091328:  Learning rate = 0.000649:   Batch Loss = 0.350064, Accuracy = 0.986328125\n",
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      "Iter #5255168:  Learning rate = 0.000599:   Batch Loss = 0.352851, Accuracy = 0.978515625\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #5279744:  Learning rate = 0.000599:   Batch Loss = 0.333618, Accuracy = 0.98828125\n",
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      "Iter #5304320:  Learning rate = 0.000575:   Batch Loss = 0.340134, Accuracy = 0.986328125\n",
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      "Iter #5361664:  Learning rate = 0.000575:   Batch Loss = 0.331896, Accuracy = 0.990234375\n",
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     ]
    },
    {
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     "text": [
      "Iter #5468160:  Learning rate = 0.000552:   Batch Loss = 0.332171, Accuracy = 0.986328125\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Iter #5656576:  Learning rate = 0.000508:   Batch Loss = 0.321305, Accuracy = 0.9921875\n",
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     ]
    },
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     "text": [
      "Iter #5844992:  Learning rate = 0.000468:   Batch Loss = 0.324378, Accuracy = 0.9921875\n",
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     ]
    },
    {
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     "text": [
      "Iter #6033408:  Learning rate = 0.000432:   Batch Loss = 0.318218, Accuracy = 0.994140625\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #6221824:  Learning rate = 0.000398:   Batch Loss = 0.324407, Accuracy = 0.98828125\n",
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     ]
    },
    {
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     "text": [
      "Iter #6410240:  Learning rate = 0.000367:   Batch Loss = 0.308033, Accuracy = 0.994140625\n",
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      "PERFORMANCE ON TEST SET:             Batch Loss = 0.355676203966, Accuracy = 0.976178050041\n",
      "Iter #6467584:  Learning rate = 0.000367:   Batch Loss = 0.327148, Accuracy = 0.98828125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.349797576666, Accuracy = 0.977221369743\n",
      "Iter #6471680:  Learning rate = 0.000367:   Batch Loss = 0.312215, Accuracy = 0.990234375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.34524294734, Accuracy = 0.981220662594\n",
      "Iter #6475776:  Learning rate = 0.000367:   Batch Loss = 0.327173, Accuracy = 0.98828125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.347919106483, Accuracy = 0.979307949543\n",
      "Iter #6479872:  Learning rate = 0.000367:   Batch Loss = 0.299694, Accuracy = 1.0\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.357632637024, Accuracy = 0.97426533699\n",
      "Iter #6483968:  Learning rate = 0.000367:   Batch Loss = 0.305983, Accuracy = 0.99609375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.361265987158, Accuracy = 0.973222076893\n",
      "Iter #6488064:  Learning rate = 0.000367:   Batch Loss = 0.329925, Accuracy = 0.986328125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.359766751528, Accuracy = 0.973222076893\n",
      "Iter #6492160:  Learning rate = 0.000367:   Batch Loss = 0.324470, Accuracy = 0.986328125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.365671515465, Accuracy = 0.970613777637\n",
      "Iter #6496256:  Learning rate = 0.000367:   Batch Loss = 0.319009, Accuracy = 0.98828125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.344833314419, Accuracy = 0.980351269245\n",
      "Iter #6500352:  Learning rate = 0.000352:   Batch Loss = 0.332251, Accuracy = 0.986328125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.355436772108, Accuracy = 0.973917603493\n",
      "Iter #6504448:  Learning rate = 0.000352:   Batch Loss = 0.300198, Accuracy = 0.994140625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.347858250141, Accuracy = 0.977569103241\n",
      "Iter #6508544:  Learning rate = 0.000352:   Batch Loss = 0.317024, Accuracy = 0.994140625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.359618782997, Accuracy = 0.97356981039\n",
      "Iter #6512640:  Learning rate = 0.000352:   Batch Loss = 0.322588, Accuracy = 0.9921875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.350380659103, Accuracy = 0.977221369743\n",
      "Iter #6516736:  Learning rate = 0.000352:   Batch Loss = 0.299874, Accuracy = 0.99609375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.342884868383, Accuracy = 0.978960156441\n",
      "Iter #6520832:  Learning rate = 0.000352:   Batch Loss = 0.323024, Accuracy = 0.9921875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.360905468464, Accuracy = 0.973743677139\n",
      "Iter #6524928:  Learning rate = 0.000352:   Batch Loss = 0.305776, Accuracy = 0.998046875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.356459468603, Accuracy = 0.975134730339\n",
      "Iter #6529024:  Learning rate = 0.000352:   Batch Loss = 0.324681, Accuracy = 0.990234375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.367868006229, Accuracy = 0.972178757191\n",
      "Iter #6533120:  Learning rate = 0.000352:   Batch Loss = 0.332227, Accuracy = 0.98828125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.346528738737, Accuracy = 0.978786289692\n",
      "Iter #6537216:  Learning rate = 0.000352:   Batch Loss = 0.319853, Accuracy = 0.986328125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.354114055634, Accuracy = 0.976873576641\n",
      "Iter #6541312:  Learning rate = 0.000352:   Batch Loss = 0.305227, Accuracy = 0.99609375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.359478592873, Accuracy = 0.97426533699\n",
      "Iter #6545408:  Learning rate = 0.000352:   Batch Loss = 0.302988, Accuracy = 0.990234375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.367636799812, Accuracy = 0.972874283791\n",
      "Iter #6549504:  Learning rate = 0.000352:   Batch Loss = 0.333924, Accuracy = 0.984375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.350758314133, Accuracy = 0.97635191679\n",
      "Iter #6553600:  Learning rate = 0.000352:   Batch Loss = 0.311039, Accuracy = 0.994140625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.353447973728, Accuracy = 0.975482523441\n",
      "Iter #6557696:  Learning rate = 0.000352:   Batch Loss = 0.332892, Accuracy = 0.98828125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.372911453247, Accuracy = 0.972526490688\n",
      "Iter #6561792:  Learning rate = 0.000352:   Batch Loss = 0.312192, Accuracy = 0.990234375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.367258369923, Accuracy = 0.969222724438\n",
      "Iter #6565888:  Learning rate = 0.000352:   Batch Loss = 0.296424, Accuracy = 0.998046875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.355661422014, Accuracy = 0.974786996841\n",
      "Iter #6569984:  Learning rate = 0.000352:   Batch Loss = 0.308408, Accuracy = 0.99609375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.362293750048, Accuracy = 0.972526490688\n",
      "Iter #6574080:  Learning rate = 0.000352:   Batch Loss = 0.308459, Accuracy = 0.9921875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.375939100981, Accuracy = 0.972874283791\n",
      "Iter #6578176:  Learning rate = 0.000352:   Batch Loss = 0.322252, Accuracy = 0.990234375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.345082253218, Accuracy = 0.978960156441\n",
      "Iter #6582272:  Learning rate = 0.000352:   Batch Loss = 0.311939, Accuracy = 0.9921875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.370315492153, Accuracy = 0.972004890442\n",
      "Iter #6586368:  Learning rate = 0.000352:   Batch Loss = 0.310719, Accuracy = 0.990234375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.367847293615, Accuracy = 0.971483230591\n",
      "Iter #6590464:  Learning rate = 0.000352:   Batch Loss = 0.312611, Accuracy = 0.9921875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.343385487795, Accuracy = 0.979134082794\n",
      "Iter #6594560:  Learning rate = 0.000352:   Batch Loss = 0.306935, Accuracy = 0.99609375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.349807798862, Accuracy = 0.976873576641\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #6598656:  Learning rate = 0.000352:   Batch Loss = 0.303213, Accuracy = 0.998046875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.342845052481, Accuracy = 0.979307949543\n",
      "Iter #6602752:  Learning rate = 0.000338:   Batch Loss = 0.316602, Accuracy = 0.9921875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.356618613005, Accuracy = 0.97426533699\n",
      "Iter #6606848:  Learning rate = 0.000338:   Batch Loss = 0.319164, Accuracy = 0.990234375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.342101633549, Accuracy = 0.979307949543\n",
      "Iter #6610944:  Learning rate = 0.000338:   Batch Loss = 0.300368, Accuracy = 0.99609375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.338129222393, Accuracy = 0.981916189194\n",
      "Iter #6615040:  Learning rate = 0.000338:   Batch Loss = 0.320529, Accuracy = 0.98828125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.343072682619, Accuracy = 0.978264629841\n",
      "Iter #6619136:  Learning rate = 0.000338:   Batch Loss = 0.305685, Accuracy = 0.994140625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.343761593103, Accuracy = 0.978960156441\n",
      "Iter #6623232:  Learning rate = 0.000338:   Batch Loss = 0.304288, Accuracy = 0.99609375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.346256196499, Accuracy = 0.977916896343\n",
      "Iter #6627328:  Learning rate = 0.000338:   Batch Loss = 0.319206, Accuracy = 0.9921875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.352637499571, Accuracy = 0.97635191679\n",
      "Iter #6631424:  Learning rate = 0.000338:   Batch Loss = 0.325431, Accuracy = 0.986328125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.356159389019, Accuracy = 0.974439203739\n",
      "Iter #6635520:  Learning rate = 0.000338:   Batch Loss = 0.315373, Accuracy = 0.990234375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.348524868488, Accuracy = 0.97704744339\n",
      "Iter #6639616:  Learning rate = 0.000338:   Batch Loss = 0.325025, Accuracy = 0.98828125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.360073983669, Accuracy = 0.973917603493\n",
      "Iter #6643712:  Learning rate = 0.000338:   Batch Loss = 0.321051, Accuracy = 0.990234375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.344460308552, Accuracy = 0.978438556194\n",
      "Iter #6647808:  Learning rate = 0.000338:   Batch Loss = 0.341192, Accuracy = 0.984375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.379942506552, Accuracy = 0.967310011387\n",
      "Iter #6651904:  Learning rate = 0.000338:   Batch Loss = 0.330327, Accuracy = 0.98828125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.385649085045, Accuracy = 0.965397298336\n",
      "Iter #6656000:  Learning rate = 0.000338:   Batch Loss = 0.326987, Accuracy = 0.986328125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.372222930193, Accuracy = 0.968353331089\n",
      "Iter #6660096:  Learning rate = 0.000338:   Batch Loss = 0.341827, Accuracy = 0.984375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.374761521816, Accuracy = 0.971135437489\n",
      "Iter #6664192:  Learning rate = 0.000338:   Batch Loss = 0.303737, Accuracy = 0.994140625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.34738740325, Accuracy = 0.97704744339\n",
      "Iter #6668288:  Learning rate = 0.000338:   Batch Loss = 0.305125, Accuracy = 0.99609375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.356508135796, Accuracy = 0.974786996841\n",
      "Iter #6672384:  Learning rate = 0.000338:   Batch Loss = 0.329145, Accuracy = 0.986328125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.361504554749, Accuracy = 0.973048150539\n",
      "Iter #6676480:  Learning rate = 0.000338:   Batch Loss = 0.339130, Accuracy = 0.986328125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.346870929003, Accuracy = 0.976525843143\n",
      "Iter #6680576:  Learning rate = 0.000338:   Batch Loss = 0.292064, Accuracy = 1.0\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.338595986366, Accuracy = 0.980177342892\n",
      "Iter #6684672:  Learning rate = 0.000338:   Batch Loss = 0.310257, Accuracy = 0.994140625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.347855091095, Accuracy = 0.976873576641\n",
      "Iter #6688768:  Learning rate = 0.000338:   Batch Loss = 0.302782, Accuracy = 0.99609375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.346290290356, Accuracy = 0.978438556194\n",
      "Iter #6692864:  Learning rate = 0.000338:   Batch Loss = 0.313310, Accuracy = 0.98828125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.349153608084, Accuracy = 0.977221369743\n",
      "Iter #6696960:  Learning rate = 0.000338:   Batch Loss = 0.337981, Accuracy = 0.98828125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.354516327381, Accuracy = 0.976004183292\n",
      "Iter #6701056:  Learning rate = 0.000324:   Batch Loss = 0.312228, Accuracy = 0.990234375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.353523701429, Accuracy = 0.974786996841\n",
      "Iter #6705152:  Learning rate = 0.000324:   Batch Loss = 0.297802, Accuracy = 0.998046875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.341771364212, Accuracy = 0.979134082794\n",
      "Iter #6709248:  Learning rate = 0.000324:   Batch Loss = 0.315165, Accuracy = 0.98828125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.347181200981, Accuracy = 0.97635191679\n",
      "Iter #6713344:  Learning rate = 0.000324:   Batch Loss = 0.311997, Accuracy = 0.9921875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.351004809141, Accuracy = 0.976525843143\n",
      "Iter #6717440:  Learning rate = 0.000324:   Batch Loss = 0.293081, Accuracy = 1.0\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.342202812433, Accuracy = 0.979481816292\n",
      "Iter #6721536:  Learning rate = 0.000324:   Batch Loss = 0.302105, Accuracy = 0.99609375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.354875147343, Accuracy = 0.97496086359\n",
      "Iter #6725632:  Learning rate = 0.000324:   Batch Loss = 0.323505, Accuracy = 0.986328125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.355220019817, Accuracy = 0.97426533699\n",
      "Iter #6729728:  Learning rate = 0.000324:   Batch Loss = 0.309833, Accuracy = 0.98828125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.349647194147, Accuracy = 0.975830316544\n",
      "Iter #6733824:  Learning rate = 0.000324:   Batch Loss = 0.309975, Accuracy = 0.994140625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.351414054632, Accuracy = 0.976004183292\n",
      "Iter #6737920:  Learning rate = 0.000324:   Batch Loss = 0.301262, Accuracy = 0.998046875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.35322457552, Accuracy = 0.976699709892\n",
      "Iter #6742016:  Learning rate = 0.000324:   Batch Loss = 0.305567, Accuracy = 0.99609375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.346003592014, Accuracy = 0.977916896343\n",
      "Iter #6746112:  Learning rate = 0.000324:   Batch Loss = 0.293104, Accuracy = 0.998046875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.349219024181, Accuracy = 0.976873576641\n",
      "Iter #6750208:  Learning rate = 0.000324:   Batch Loss = 0.300327, Accuracy = 0.994140625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.359121590853, Accuracy = 0.973048150539\n",
      "Iter #6754304:  Learning rate = 0.000324:   Batch Loss = 0.303322, Accuracy = 0.99609375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.351933687925, Accuracy = 0.976178050041\n",
      "Iter #6758400:  Learning rate = 0.000324:   Batch Loss = 0.294836, Accuracy = 0.998046875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.342962324619, Accuracy = 0.977743029594\n",
      "Iter #6762496:  Learning rate = 0.000324:   Batch Loss = 0.299955, Accuracy = 0.9921875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.351522356272, Accuracy = 0.975482523441\n",
      "Iter #6766592:  Learning rate = 0.000324:   Batch Loss = 0.294166, Accuracy = 0.998046875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.346992492676, Accuracy = 0.977221369743\n",
      "Iter #6770688:  Learning rate = 0.000324:   Batch Loss = 0.309330, Accuracy = 0.98828125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.348911821842, Accuracy = 0.976873576641\n",
      "Iter #6774784:  Learning rate = 0.000324:   Batch Loss = 0.299446, Accuracy = 0.994140625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.349592983723, Accuracy = 0.976178050041\n",
      "Iter #6778880:  Learning rate = 0.000324:   Batch Loss = 0.320204, Accuracy = 0.98828125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.343533396721, Accuracy = 0.979655683041\n",
      "Iter #6782976:  Learning rate = 0.000324:   Batch Loss = 0.316101, Accuracy = 0.990234375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.346482902765, Accuracy = 0.978264629841\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #6787072:  Learning rate = 0.000324:   Batch Loss = 0.306883, Accuracy = 0.98828125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.351491391659, Accuracy = 0.976525843143\n",
      "Optimization Finished!\n",
      "FINAL RESULT: Batch Loss = 0.351491391659, Accuracy = 0.976525843143\n",
      "TOTAL TIME:  1240.38658214\n"
     ]
    }
   ],
   "source": [
    "test_losses = []\n",
    "test_accuracies = []\n",
    "train_losses = []\n",
    "train_accuracies = []\n",
    "sess = tf.InteractiveSession(config=tf.ConfigProto(log_device_placement=True))\n",
    "init = tf.global_variables_initializer()\n",
    "sess.run(init)\n",
    "\n",
    "# Perform Training steps with \"batch_size\" amount of data at each loop. \n",
    "# Elements of each batch are chosen randomly, without replacement, from X_train, \n",
    "# restarting when remaining datapoints < batch_size\n",
    "step = 1\n",
    "time_start = time.time()\n",
    "unsampled_indices = range(0,len(X_train))\n",
    "\n",
    "while step * batch_size <= training_iters:\n",
    "    #print (sess.run(learning_rate)) #decaying learning rate\n",
    "    #print (sess.run(global_step)) # global number of iterations\n",
    "    if len(unsampled_indices) < batch_size:\n",
    "        unsampled_indices = range(0,len(X_train)) \n",
    "    batch_xs, raw_labels, unsampled_indicies = extract_batch_size(X_train, y_train, unsampled_indices, batch_size)\n",
    "    batch_ys = one_hot(raw_labels)\n",
    "    # check that encoded output is same length as num_classes, if not, pad it \n",
    "    if len(batch_ys[0]) < n_classes:\n",
    "        temp_ys = np.zeros((batch_size, n_classes))\n",
    "        temp_ys[:batch_ys.shape[0],:batch_ys.shape[1]] = batch_ys\n",
    "        batch_ys = temp_ys\n",
    "       \n",
    "    \n",
    "\n",
    "    # Fit training using batch data\n",
    "    _, loss, acc = sess.run(\n",
    "        [optimizer, cost, accuracy],\n",
    "        feed_dict={\n",
    "            x: batch_xs, \n",
    "            y: batch_ys\n",
    "        }\n",
    "    )\n",
    "    train_losses.append(loss)\n",
    "    train_accuracies.append(acc)\n",
    "    \n",
    "    # Evaluate network only at some steps for faster training: \n",
    "    if (step*batch_size % display_iter == 0) or (step == 1) or (step * batch_size > training_iters):\n",
    "        \n",
    "        # To not spam console, show training accuracy/loss in this \"if\"\n",
    "        print(\"Iter #\" + str(step*batch_size) + \\\n",
    "              \":  Learning rate = \" + \"{:.6f}\".format(sess.run(learning_rate)) + \\\n",
    "              \":   Batch Loss = \" + \"{:.6f}\".format(loss) + \\\n",
    "              \", Accuracy = {}\".format(acc))\n",
    "        \n",
    "        # Evaluation on the test set (no learning made here - just evaluation for diagnosis)\n",
    "        loss, acc = sess.run(\n",
    "            [cost, accuracy], \n",
    "            feed_dict={\n",
    "                x: X_test,\n",
    "                y: one_hot(y_test)\n",
    "            }\n",
    "        )\n",
    "        test_losses.append(loss)\n",
    "        test_accuracies.append(acc)\n",
    "        print(\"PERFORMANCE ON TEST SET:             \" + \\\n",
    "              \"Batch Loss = {}\".format(loss) + \\\n",
    "              \", Accuracy = {}\".format(acc))\n",
    "\n",
    "    step += 1\n",
    "\n",
    "print(\"Optimization Finished!\")\n",
    "\n",
    "# Accuracy for test data\n",
    "\n",
    "one_hot_predictions, accuracy, final_loss = sess.run(\n",
    "    [pred, accuracy, cost],\n",
    "    feed_dict={\n",
    "        x: X_test,\n",
    "        y: one_hot(y_test)\n",
    "    }\n",
    ")\n",
    "\n",
    "test_losses.append(final_loss)\n",
    "test_accuracies.append(accuracy)\n",
    "\n",
    "print(\"FINAL RESULT: \" + \\\n",
    "      \"Batch Loss = {}\".format(final_loss) + \\\n",
    "      \", Accuracy = {}\".format(accuracy))\n",
    "time_stop = time.time()\n",
    "print(\"TOTAL TIME:  {}\".format(time_stop - time_start))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1659\n",
      "13256\n"
     ]
    },
    {
     "data": {
      "image/png": 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Kq93Udt9zI5S7u//TjKIoyUKIG8iVwbJIFU5328uQ4KMZ8By0sbkvbBsJm/86mr6xi5qK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd762a1b750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing Accuracy: 97.6525843143%\n",
      "\n",
      "Precision: 97.639943676%\n",
      "Recall: 97.6525821596%\n",
      "f1_score: 97.6427318211%\n",
      "\n",
      "Confusion Matrix:\n",
      "Created using test set of 5751 datapoints, normalised to % of each class in the test dataset\n"
     ]
    },
    {
     "data": {
      "image/png": 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53ZDWmzqXW9JOgOccmK91w/UXnsXyP541f0FWTLryNVZeXPgMsNdAZkr/7LS3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd762813650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# (Inline plots: )\n",
    "%matplotlib inline\n",
    "\n",
    "font = {\n",
    "    'family' : 'Bitstream Vera Sans',\n",
    "    'weight' : 'bold',\n",
    "    'size'   : 18\n",
    "}\n",
    "matplotlib.rc('font', **font)\n",
    "\n",
    "width = 12\n",
    "height = 12\n",
    "plt.figure(figsize=(width, height))\n",
    "\n",
    "indep_train_axis = np.array(range(batch_size, (len(train_losses)+1)*batch_size, batch_size))\n",
    "#plt.plot(indep_train_axis, np.array(train_losses),     \"b--\", label=\"Train losses\")\n",
    "plt.plot(indep_train_axis, np.array(train_accuracies), \"g--\", label=\"Train accuracies\")\n",
    "\n",
    "indep_test_axis = np.append(\n",
    "    np.array(range(batch_size, len(test_losses)*display_iter, display_iter)[:-1]),\n",
    "    [training_iters]\n",
    ")\n",
    "#plt.plot(indep_test_axis, np.array(test_losses), \"b-\", linewidth=2.0, label=\"Test losses\")\n",
    "plt.plot(indep_test_axis, np.array(test_accuracies), \"b-\", linewidth=2.0, label=\"Test accuracies\")\n",
    "print len(test_accuracies)\n",
    "print len(train_accuracies)\n",
    "\n",
    "plt.title(\"Training session's Accuracy over Iterations\")\n",
    "plt.legend(loc='lower right', shadow=True)\n",
    "plt.ylabel('Training Accuracy')\n",
    "plt.xlabel('Training Iteration')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# Results\n",
    "\n",
    "predictions = one_hot_predictions.argmax(1)\n",
    "\n",
    "print(\"Testing Accuracy: {}%\".format(100*accuracy))\n",
    "\n",
    "print(\"\")\n",
    "print(\"Precision: {}%\".format(100*metrics.precision_score(y_test, predictions, average=\"weighted\")))\n",
    "print(\"Recall: {}%\".format(100*metrics.recall_score(y_test, predictions, average=\"weighted\")))\n",
    "print(\"f1_score: {}%\".format(100*metrics.f1_score(y_test, predictions, average=\"weighted\")))\n",
    "\n",
    "print(\"\")\n",
    "print(\"Confusion Matrix:\")\n",
    "print(\"Created using test set of {} datapoints, normalised to % of each class in the test dataset\".format(len(y_test)))\n",
    "confusion_matrix = metrics.confusion_matrix(y_test, predictions)\n",
    "\n",
    "\n",
    "#print(confusion_matrix)\n",
    "normalised_confusion_matrix = np.array(confusion_matrix, dtype=np.float32)/np.sum(confusion_matrix)*100\n",
    "\n",
    "\n",
    "# Plot Results: \n",
    "width = 12\n",
    "height = 12\n",
    "plt.figure(figsize=(width, height))\n",
    "plt.imshow(\n",
    "    normalised_confusion_matrix, \n",
    "    interpolation='nearest', \n",
    "    cmap=plt.cm.Blues\n",
    ")\n",
    "plt.title(\"Confusion matrix \\n(normalised to % of total test data)\")\n",
    "plt.colorbar()\n",
    "tick_marks = np.arange(n_classes)\n",
    "plt.xticks(tick_marks, LABELS, rotation=90)\n",
    "plt.yticks(tick_marks, LABELS)\n",
    "plt.tight_layout()\n",
    "plt.ylabel('True label')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "#X_val_path = DATASET_PATH + \"X_val.txt\"\n",
    "#X_val = load_X(X_val_path)\n",
    "#print X_val\n",
    "#\n",
    "#preds = sess.run(\n",
    "#    [pred],\n",
    "#    feed_dict={\n",
    "#        x: X_val\n",
    "#   }\n",
    "#)\n",
    "#\n",
    "#print preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "#sess.close()\n",
    "print test_accuracies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "Final accuracy of >90% is pretty good, considering that training takes about 7 minutes.\n",
    "\n",
    "Noticeable confusion between activities of Clapping Hands and Boxing, and between Jumping Jacks and Waving Two Hands which is understandable.\n",
    "\n",
    "In terms of the applicability of this to a wider dataset, I would imagine that it would be able to work for any activities in which the training included a views from all angles to be tested on. It would be interesting to see it's applicability to camera angles in between the 4 used in this dataset, without training on them specifically.\n",
    "\n",
    " Overall, this experiment validates the idea that 2D pose can be used for at least human activity recognition, and provides verification to continue onto use of 2D pose for behaviour estimation in both people and animals\n",
    " \n",
    "\n",
    " ### With regards to Using LSTM-RNNs\n",
    " - Batch sampling\n",
    "     - It is neccessary to ensure you are not just sampling classes one at a time! (ie y_train is ordered by class and batch chosen in order)The use of random sampling of batches without replacement from the training data resolves this.    \n",
    " \n",
    " - Architecture\n",
    "     - Testing has been run using a variety of hidden units per LSTM cell, with results showing that testing accuracy achieves a higher score when using a number of hidden cells approximately equal to that of the input, ie 34. The following figure displays the final accuracy achieved on the testing dataset for a variety of hidden units, all using a batch size of 4096 and 300 epochs (a total of 1657 iterations, with testing performed every 8th iteration).\n",
    "   \n",
    " \n",
    " \n",
    "\n",
    "## Future Works\n",
    "\n",
    "Inclusion of :\n",
    "\n",
    " - A pipeline for qualitative results\n",
    " - A validation dataset\n",
    " - Momentum     \n",
    " - Normalise input data (each point with respect to distribution of itself only)\n",
    " - Dropout\n",
    " - Comparison of effect of changing batch size\n",
    " \n",
    "\n",
    "Further research will be made into the use on more subtle activity classes, such as walking versus running, agitated movement versus calm movement, and perhaps normal versus abnormal behaviour, based on a baseline of normal motion.\n",
    "\n",
    "\n",
    "## References\n",
    "\n",
    "The dataset can be found at http://tele-immersion.citris-uc.org/berkeley_mhad released under the BSD-2 license\n",
    ">Copyright (c) 2013, Regents of the University of California All rights reserved.\n",
    "\n",
    "The network used in this experiment is based on the following, available under the [MIT License](https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition/blob/master/LICENSE). :\n",
    "> Guillaume Chevalier, LSTMs for Human Activity Recognition, 2016\n",
    "> https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NbConvertApp] Converting notebook LSTM.ipynb to markdown\n",
      "[NbConvertApp] Writing 303430 bytes to LSTM.md\n"
     ]
    }
   ],
   "source": [
    "# Let's convert this notebook to a README for the GitHub project's title page:\n",
    "!jupyter nbconvert --to markdown LSTM.ipynb\n",
    "!mv LSTM.md README.md"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## "
   ]
  }
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